Power management in energy harvesting sensor networks
ACM Transactions on Embedded Computing Systems (TECS) - Special Section LCTES'05
Wireless sensor network survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Simulating wireless and mobile networks in OMNeT++ the MiXiM vision
Proceedings of the 1st international conference on Simulation tools and techniques for communications, networks and systems & workshops
Proceedings of the 6th ACM conference on Embedded network sensor systems
Joint energy management and resource allocation in rechargeable sensor networks
INFOCOM'10 Proceedings of the 29th conference on Information communications
Opportunistic routing in wireless sensor networks powered by ambient energy harvesting
Computer Networks: The International Journal of Computer and Telecommunications Networking
Collaborative mobile charging for sensor networks
MASS '12 Proceedings of the 2012 IEEE 9th International Conference on Mobile Ad-Hoc and Sensor Systems (MASS)
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Wireless sensor networks are increasingly using energy harvesting to extend their lifetime and avoid battery replacement. However, ambient energy sources typically exhibit temporal-spatial variation, and complex power management algorithms have been proposed to model and adapt to variation and achieve energy-neutral operation. However, existing algorithms are limited in the scale of spatial variation that they can accommodate, as they are restricted by the physical boundaries of the network. This paper proposes Opportunistic Energy Trading (OET) to overcome this limitation, and allow networks to trade energy to neighbouring networks which may either be heavily energy-constrained or else suffering from a temporary drought of harvested-energy. To show the potential of OET, we present a case study consisting of an energy-constrained battery-powered WSN which neighbours an energy-harvesting WSN. The case study considers a simplified version of OET, whereby the harvesting WSN transfers (i.e. trades for free) its excess energy to the constrained WSN in order to extend its lifetime. The case study is evaluated through simulation, and shows that the lifetime of the energy-constrained network increases by 40% while the effects on the harvesting network can be considered insignificant.